Auto Clustering Prediction Models

2. The method of claim 1, wherein the different types of features comprise one or more of product related features, store features, timing related features, or promotions.

4. The method of claim 1, wherein the training each of the clusters comprises running a linear regression on each cluster.

5. The method of claim 1, further comprising saving each of trained cluster models into a binary file.

6. The method of claim 1, the using the promotion effect to forecast demand comprising: demand=base demand*promotion lift*price lift.

7. The method of claim 1, wherein the first product comprises a single stock-keeping unit (SKU) and the historical sales data comprises all SKUs sold at all locations for an entity.

10. The computer-readable medium of claim 9, wherein the different types of features comprise one or more of product related features, store features, timing related features, or promotions.

12. The computer-readable medium of claim 9, wherein the training each of the clusters comprises running a linear regression on each cluster.

13. The computer-readable medium of claim 9, the predicting further comprising saving each of trained cluster models into a binary file.

14. The computer-readable medium of claim 9, the using the promotion effect to forecast demand comprising: demand=base demand*promotion lift*price lift.

15. The computer-readable medium of claim 9, wherein the first product comprises a single stock-keeping unit (SKU) and the historical sales data comprises all SKUs sold at all locations for an entity.

18. The product demand forecasting system of claim 17, wherein the different types of features comprise one or more of product related features, store features, timing related features, or promotions.

20. The product demand forecasting system of claim 17, wherein the training each of the clusters comprises running a linear regression on each cluster.